Converting JSON to CSV, XML, and YAML: A Developer's Guide

Example: Converting User Data

{
  "users": [
    {
      "id": 1,
      "name": "John Doe",
      "email": "john@example.com",
      "age": 30,
      "city": "New York",
      "active": true
    },
    {
      "id": 2,
      "name": "Jane Smith", 
      "email": "jane@example.com",
      "age": 25,
      "city": "San Francisco",
      "active": false
    },
    {
      "id": 3,
      "name": "Bob Johnson",
      "email": "bob@example.com", 
      "age": 35,
      "city": "Chicago",
      "active": true
    }
  ]
}

id,name,email,age,city,active
1,"John Doe","john@example.com",30,"New York",true
2,"Jane Smith","jane@example.com",25,"San Francisco",false
3,"Bob Johnson","bob@example.com",35,"Chicago",true

Best Practices for JSON to CSV:

• Ensure all objects have the same structure for consistent columns
• Handle missing properties by providing default values
• Flatten nested objects using dot notation (e.g., "address.city")
• Convert arrays to delimited strings or separate columns
• Use proper CSV escaping for special characters

Example: Converting Product Data

{
  "product": {
    "id": 123,
    "name": "Laptop Computer",
    "category": "Electronics",
    "price": 999.99,
    "inStock": true,
    "tags": ["laptop", "computer", "electronics"],
    "specifications": {
      "cpu": "Intel i7",
      "memory": "16GB",
      "storage": "512GB SSD"
    }
  }
}

<?xml version="1.0" encoding="UTF-8"?>
<product>
  <id>123</id>
  <name>Laptop Computer</name>
  <category>Electronics</category>
  <price>999.99</price>
  <inStock>true</inStock>
  <tags>
    <tag>laptop</tag>
    <tag>computer</tag>
    <tag>electronics</tag>
  </tags>
  <specifications>
    <cpu>Intel i7</cpu>
    <memory>16GB</memory>
    <storage>512GB SSD</storage>
  </specifications>
</product>

XML Conversion Strategies:

• Array Handling: Convert JSON arrays to repeated XML elements
• Root Element: Wrap content in a root element for valid XML
• Attribute vs Element: Decide whether to use attributes or child elements
• Naming Conventions: Ensure XML element names are valid identifiers
• Type Information: Consider adding type attributes for data types

Example: Converting Configuration Data

{
  "app": {
    "name": "MyApplication",
    "version": "1.2.3",
    "environment": "production"
  },
  "database": {
    "host": "localhost",
    "port": 5432,
    "name": "myapp_db",
    "ssl": true
  },
  "features": {
    "authentication": true,
    "analytics": false,
    "caching": {
      "enabled": true,
      "ttl": 3600,
      "providers": ["redis", "memory"]
    }
  }
}

app:
  name: MyApplication
  version: 1.2.3
  environment: production

database:
  host: localhost
  port: 5432
  name: myapp_db
  ssl: true

features:
  authentication: true
  analytics: false
  caching:
    enabled: true
    ttl: 3600
    providers:
      - redis
      - memory

YAML Conversion Best Practices:

• Use consistent indentation (2 spaces recommended)
• Be careful with special characters and reserved words
• Consider adding comments for complex configurations
• Use proper quoting for strings with special characters
• Test the converted YAML for syntax validity

Handling Complex Data Structures

Batch Conversion Strategies

# Example: Batch conversion script
#!/bin/bash

# Convert all JSON files in a directory to CSV
for file in *.json; do
  filename=$(basename "$file" .json)
  echo "Converting $file to ${filename}.csv"
  # Use your preferred conversion tool here
  json2csv "$file" > "${filename}.csv"
done

# Convert to XML with validation
for file in *.json; do
  filename=$(basename "$file" .json)
  echo "Converting $file to ${filename}.xml"
  json2xml "$file" | xmllint --format - > "${filename}.xml"
done

Quality Assurance

• Validation: Always validate the converted output format
• Round-trip Testing: Test converting back to JSON to ensure data integrity
• Schema Verification: Use schemas to validate structure and data types
• Sample Testing: Test with representative data samples
• Error Handling: Implement proper error handling for malformed data